Determining an effect on dissemination of information related to an event based on a dynamic confidence level associated with the event

ABSTRACT

Methods and apparatus related to determining an effect on dissemination of information related to an event based on a dynamic confidence level associated with the event. For example, an event and an event confidence level of the event may be determined based on a message of a user. An effect on dissemination of information related to the event may be determined based on the confidence level. A new confidence level may be determined based on additional data associated with the event and the effect on dissemination of information may be adjusted based on the new confidence level. In some implementations, the additional data may be based on a new message that is related to the message, such as a reply to the message.

BACKGROUND

A user may create a message related to an event and send the message toone or more other users. The user, and/or one or more of the otherusers, may send a reply to the message to further plan the event.

SUMMARY

This specification is generally directed to methods and apparatusrelated to determining an effect on dissemination of information relatedto an event based on a dynamic confidence level associated with theevent. For example, an event and an event confidence level of the eventmay be determined based on a message of a user. An effect ondissemination of information related to the event may be determinedbased on the confidence level. A new confidence level may be determinedbased on additional data associated with the event and the effect ondissemination of information may be adjusted based on the new confidencelevel. In some implementations, the additional data may be based on anew message that is related to the message, such as a reply to themessage.

In some implementations, determining the effect on dissemination ofinformation related to the event may include determining whetherinformation related to the event is provided and/or determining whetherand/or to what extent information related to the event is influenced bythe event. Adjusting the effect may include adjusting whetherinformation related to the event is provided and/or whether and/or towhat extent to which information related to the event is influenced bythe event. In some implementations, the dissemination of informationincludes at least a first dissemination of information and a seconddissemination of information, and determining and/or adjusting theeffect includes determining an effect on the first dissemination ofinformation and determining an effect on the second dissemination ofinformation.

In some implementations, a method is provided that includes the stepsof: identifying a message of a user, wherein the message includes aplurality of terms; determining an event based on the message, whereinthe event includes one or more event properties that are determinedbased on one or more of the terms; determining an event confidence levelbased on the event properties; determining an effect on dissemination ofinformation related to the event, wherein the effect is determined basedon the event confidence level; identifying additional data associatedwith the user and the event; determining a new event confidence levelbased on the additional data; and adjusting the effect on disseminationof information related to the event based on the new event confidencelevel.

This method and other implementations of technology disclosed herein mayeach optionally include one or more of the following features.

The event properties may be related to one or more of attendees of theevent, event location, event type, and event time.

The step of identifying the additional data may include identifying anew message of the user that is associated with the message and that isreceived subsequent to the message.

The additional data may be based on one or more actions of the user. Theadditional data may be based on a submitted search query of a usersearch query action of the one or more actions.

The dissemination of information may include a first dissemination ofinformation and a second dissemination of information unique from thefirst dissemination of information, wherein the step of determining theeffect may include the step of determining, based on the confidencelevel, to influence the first dissemination of information based on theevent and to not influence the second dissemination of information basedon the event and wherein the step of adjusting the effect may includethe step of determining, based on the new confidence level, to influenceboth the first dissemination of information and the second disseminationof information based on the event.

The dissemination of information may include providing one or moresearch results to the user; wherein the step of determining the effectmay include the step of determining, based on the confidence level, notto rank one or more of the search results based on the event; andwherein the step of adjusting the effect may include the step ofdetermining, based on the new confidence level, to rank one or more ofthe search results based on the event.

The dissemination of information may include providing one or moresearch results to the user; wherein the step of determining the effectmay include determining, based on the confidence level, a first extentto which one or more of the search results is ranked based on the event;and wherein the step of adjusting the effect may include determining,based on the new confidence level, a second extent to which one or moreof the search results is ranked based on the event.

The dissemination of information may include providing one or more querysuggestions to the user and the step of adjusting the effect may includeadjusting, based on the new confidence level, a degree of influence ofthe event in ranking the query suggestions.

The new confidence level may be determined based on the confidencelevel.

The dissemination of information may include providing a notification tothe user and the effect may be whether to provide the notification tothe user. The dissemination of information may include providing anotification to the user and the effect may include one or morecharacteristics of the notification to the user.

The dissemination of information may include a first dissemination ofinformation from a first application and a second dissemination ofinformation from a second application unique from the first application;wherein determining the effect may include determining, based on theconfidence level, the effect on the first dissemination of informationbased on first criteria and determining the effect on the seconddissemination of information based on second criteria unique from thefirst criteria.

Other implementations may include a non-transitory computer readablestorage medium storing instructions executable by a processor to performa method such as one or more of the methods described herein. Yetanother implementation may include a system including memory and one ormore processors operable to execute instructions, stored in the memory,to perform a method such as one or more of the methods described herein.

Particular implementations of the subject matter described hereindetermine, for a user, a confidence level for an event associated withone or more messages of the user and adjust an effect on disseminationof information related to the event based on the confidence level. Theconfidence level may be determined for a user based on one or more termsin one or more messages associated with the user and/or based on otherdata that is associated with the user. The effect on dissemination ofinformation related to the event may include whether the information isprovided to the user and/or whether and/or to what extent theinformation is influenced by the event.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts discussed in greater detail herein arecontemplated as being part of the subject matter disclosed herein. Forexample, all combinations of claimed subject matter appearing at the endof this disclosure are contemplated as being part of the subject matterdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which an effecton dissemination of information related to an event may be determinedbased on a dynamic confidence level associated with the event.

FIG. 2 is a flow chart illustrating an example method of determining aneffect on dissemination of information related to an event.

FIG. 3 is an illustration of an example message.

FIG. 4 is an illustration of an example notification related to anevent.

FIG. 5 is an illustration of another example notification related to anevent.

FIG. 6A is an illustration of an example of providing query suggestionresults.

FIG. 6B is an illustration of an example of providing query suggestionresults, wherein the ranking of the query suggestions is influenced byan event.

FIG. 7 illustrates a block diagram of an example computer system.

DETAILED DESCRIPTION

A user may send and/or receive one or more messages that are associatedwith an event. For example, a user may send a message to one or moreother users and provide information related to an event in the message.A message that is related to an event may include information that isrelated to one or more event properties, such as people associated withthe event, event location(s), event time(s), event date(s), and/or eventtype(s). As another example, a user may receive a message that isrelated to planning an event and the user and/or other recipients of themessage may reply with related messages.

One or more messages of a user may be utilized to determine, for theuser, an event and an event confidence level for the event. The eventand the event confidence level may be determined based on informationassociated with the messages, such as terms of the messages that areassociated with one or more event properties. As described herein, aneffect on dissemination of information related to the event may bedetermined based on the event confidence level. Moreover, the effect ondissemination of information related to the event may be adjusted basedon determination of a new confidence level based on additional datarelated to the event, such as subsequent messages related to the one ormore messages on which the initial confidence level was based.

As described herein, dissemination of information related to an eventmay include, for example, providing a user with search results relatedto the event, providing a user with query suggestions related to theevent, and/or providing the user with notifications related to theevent. Determining and/or adjusting the effect based on a confidencelevel may include, for example, determining whether and/or to whatextent to influence search results related to the event, determiningwhether and/or to what extent to influence query suggestions related tothe event, and/or determining whether to provide a notification relatedto the event and/or determining what type of notification to provide.

As one example, a first message of a user (sent or received by the user)may include one or more terms that are indicative of an event type, suchas “party,” “meeting,” and/or “dinner.” An event that includes an eventproperty (event type) may be determined based on the presence of suchterms in the first message. An event confidence level may be determinedbased on the event property and/or other information associated with thefirst message. The event confidence level is generally indicative of alikelihood that the user has interest in the event. For example, theevent confidence level may be indicative of whether the first messageindicates an event (which generally indicates a likelihood that the userhas interest in the event). In some implementations, a greater number ofevent properties and/or a greater prominence (e.g., frequency, position)of event properties in a message will result in a determined confidencelevel that is more indicative of confidence than a determined confidencelevel based on a lesser number of event properties and/or a lesserprominence of event properties in a message. An effect on disseminationof information related to the event may be determined based on the eventconfidence level.

Another message of the user that is related to the first message may bereceived subsequent to the first message. For example, a second messagemay be a reply to the first message and may be sent by the user and/orone or more other users. The second message may include additionalinformation related to the determined event. For example, the secondmessage may provide additional event properties related to the eventsuch as a date and/or location for the event. A new event confidencelevel may be determined based on the additional information of thesecond message and, optionally, based on the information of the firstmessage. The effect on dissemination of information related to the eventmay be adjusted based on the new event confidence level. For example,the new event confidence level may be more indicative of confidence thanthe confidence level (due to the additional event properties) and, as aresult, more information may be influenced by the event and/orinformation may be influenced to a greater extent by the event.

Referring to FIG. 1, a block diagram of an example environment isillustrated in which an effect on dissemination of information relatedto an event may be determined based on a dynamic confidence levelassociated with the event. The environment includes computing device105, content database 115, confidence determination engine 125, andapplication system 130. The environment also includes a communicationnetwork 101 that enables communication between various components of theenvironment. In some implementations, the communication network 101 mayinclude the Internet, one or more intranets, and/or one or more bussubsystems. The communication network 101 may optionally utilize one ormore standard communications technologies, protocols, and/orinter-process communication techniques.

The computing device 105, content database 115, confidence determinationengine 125, and/or application system 130 of the example environment ofFIG. 1 may each include memory for storage of data and softwareapplications, a processor for accessing data and executing applications,and components that facilitate communication over a network. In someimplementations, computing device 105, content database 115, confidencedetermination engine 125, and/or application system 130 may each includehardware that shares one or more characteristics with the examplecomputer system that is illustrated in FIG. 7. The operations performedby components of the example environment may be distributed acrossmultiple computer systems. For example, content database 115, confidencedetermination engine 125, and/or application system 130 may be computerprograms running on one or more computers in one or more locations thatare coupled to each other through a network.

The computing device 105 executes one or more applications and may be,for example, a desktop computer, a laptop computer, a cellular phone, asmartphone, a personal digital assistant (PDA), a tablet computer, anavigation system, a wearable computer device (e.g., glasses, watch,earpiece), and/or other computing device. The computing device 105 maybe utilized by a user to, for example: compose one or more messages suchas those described herein; view one or more messages such as thosedescribed herein; and/or receive information related to an event such asinformation described herein.

In some implementations, content database 115 and/or additionaldatabases may be utilized by one or more components to store and/oraccess information related to one or more messages, events, eventproperties, query suggestions, search results, and/or one or morenotifications that may be associated with events. For example, adetermined event may be stored in, and accessed from, content database115. The event may be associated with a user and with one or more eventproperties, such as attendees of the event, the event location, theevent type, and/or the event temporal values. A notification associatedwith the event may be determined based on information stored in contentdatabase 115 such as information related to the event properties of theevent. A notification may include, for example, a notification that maybe provided to the user via computing device 105 as a reminder for theevent one hour before the event start time. Information described hereinmay optionally be stored in the content database 115 and/or anadditional database. For example, search results and/or querysuggestions described herein may be stored in a separate database andone or more event properties that may be utilized to influence theranking of the search results and/or query suggestions may be stored inthe content database 115.

The content database 115 may include one or more storage mediums. Forexample, in some implementations, the content database 115 may includemultiple computer servers each containing one or more storage mediums.In this specification, the term “database” will be used broadly to referto any collection of data. The data of the database does not need to bestructured in any particular way, or structured at all, and it can bestored on storage devices in one or more locations. Thus, for example,the database may include multiple collections of data, each of which maybe organized and accessed differently. In some implementations, thecontent database 115 may be a database that contains only content of agiven user and that is personal to the given user. In someimplementations, the content database 115 may be a database thatincludes content of multiple users, with access restrictions that onlyenable access to a given users' content by the given user and/or one ormore other users and/or components (e.g., confidence determinationengine 125 and/or application system 130) that are authorized by thegiven user.

In some implementations, content database 115 and/or another databasemay be utilized to identify and/or store one or more entities. Forexample, content database 115 may include, for each of a plurality ofentities, a mapping (e.g., data defining an association) between theentity and one or more attributes and/or other related entities. In someimplementations, entities are topics of discourse. In someimplementations, entities are persons, places, concepts, and/or thingsthat can be referred to by a textual representation (e.g., a term orphrase) and are distinguishable from one another (e.g., based oncontext). For example, the text “bush” in a query or on a webpage maypotentially refer to multiple entities such as President George HerbertWalker Bush, President George Walker Bush, a shrub, and the rock bandBush.

In some implementations, an entity may be referenced by a unique entityidentifier that may be used to identify the entity. The unique entityidentifier may be associated with one or more attributes associated withthe entity and/or with other entities. For example, in someimplementations, the content database 115 may include attributesassociated with unique identifiers of one or more entities. For example,a unique identifier for the entity associated with the airport with anairport code “LAX” may be associated with a name or alias attribute of“LAX,” another alias attribute of “Los Angeles International Airport”(an alternative name by which LAX is often referenced), a phone numberattribute, an address attribute, and/or an entity type attribute of“airport” in the entity database. Additional and/or alternativeattributes may be associated with an entity in one or more databases.

In some implementations, a stored event in content database 115 mayinclude one or more event properties that are related to the event, suchas event location information, a start time, an end time, an event date,an event type, and/or one or more attendees of the event. Events may becreated by a user and/or by one or more components that may identifyinformation associated with a user and create the event based on theidentified information. For example, as described herein, confidencedetermination engine 125 may identify a message of a user and create anevent based on one or more terms in the message.

In some implementations, content database 115 may include one or moremessages that are associated with the user, such as messages that werecomposed by the user, messages that were sent by the user, and/ormessages that were received by the user from one or more other users. Asused herein, a message is an electronic communication between two ormore users. A message includes one or more terms and an indication of asender and one or more recipients. Messages may include, for example,emails, text messages, social media postings, instant messages, and/ormessage board postings. In some implementations, a message may be amessage trail of one or more related messages. For example, a messagemay be a message trail that includes an original message sent from User1 to User 2 and a reply to the message sent from User 2 to User 1. Insome implementations, a message may include multiple recipients. Forexample, User 1 may create a message and provide the message to bothUser 2 and User 3.

Confidence determination engine 125 may identify one or more messages ofa user. In some implementations, confidence determination engine 125 mayidentify one or more messages from content database 115. For example,content database 115 may include one or more messages that have beensent and/or received by the user. In some implementations, confidencedetermination engine 125 may be a component of a messaging system andmay identify messages as they are sent and/or received by the user. Forexample, confidence determination engine 125 may be a component of anemail system and confidence determination engine 125 may identify emailmessages as they are created, sent, and/or received by the user.

For an identified message, confidence determination engine 125 maydetermine if the message is associated with an event and, if so,determine an event and an event confidence level based on the message.For example, the confidence determination engine 125 may determine theidentified message is associated with an event if the identified messageincludes one or more terms that are indicative of event propertiesand/or based on other factors such as the number of recipients of themessages, the number of related messages, and/or attributes of therecipients. If the identified message is associated with an event, theconfidence determination engine 125 may determine an event for the userthat includes one or more event properties that are determined based onthe identified message. The confidence determination engine 125 may alsodetermine an event confidence level based on the identified message andassociate the event confidence level with the event. The event and theevent confidence level may be stored as an entry in the content database115. In this specification, the term “entry” will be used broadly torefer to any mapping of a plurality of associated information items. Asingle entry need not be present in a single storage device and mayinclude pointers or other indications of information items that may bepresent on other storage devices.

In some implementations, confidence determination engine 125 mayidentify one or more terms in an identified message and utilize theterms to determine an event and/or an event confidence level. Terms in amessage may include, for example, one or more terms in the body, subjectheadings, and/or one or more sender and/or recipient identifiers. Forexample, confidence determination engine 125 may identify“joe@exampleurl.com,” the email address of the sender of an email, as aterm in the email. Also, for example, confidence determination engine125 may identify “Bob's Birthday Party” in the subject line and/or inthe body of an e-mail as terms of the email. As described herein, aneffect on dissemination of information related to the event may bedetermined based on the event confidence level. Moreover, the effect ondissemination of information related to the event may be adjusted basedon determination of a new confidence level based on additional datarelated to the event, such as subsequent messages related to the one ormore messages on which the initial confidence level was based.

Confidence determination engine 125 may utilize one or more techniquesto determine an event and/or confidence level based on identified terms.In some implementations, confidence determination engine 125 mayidentify terms of the message that are associated with entities that arerelated to one or more event properties and utilize those terms and/orentities to determine event properties of an event and/or determine anevent confidence level. For example, confidence determination engine 125may identify one or more terms that are aliases of entities that areassociated with a “people” entity in content database 115, andconfidence determination engine 125 may determine that the terms thatare associated with a “people” entity are related to an “eventattendees” event property. Also, for example, confidence determinationengine 125 may identify one or more terms that are associated with a“places” entity in content database 115, such as identifying arelationship between an entity with an alias of “Restaurant 1” and a“places” entity, and determine that the identified terms may be relatedto an “event location” event property. Also, for example, confidencedetermination engine 125 may identify one or more terms that areassociated with an “event type” entity in content database 115, such asidentifying a relationship between an entity with an alias of “dinner”and an “event type” entity, and determine that the identified terms maybe related to an “event type” event property.

In some implementations, confidence determination engine 125 mayidentify one or more terms that are in a format that is indicative ofinformation that may be related to an event. For example, confidencedetermination engine 125 may identify “11/11/13” in a message anddetermine that, based on the term having a format that is indicative ofa date (XX/XX/XX), the term may be related to an “event date” eventproperty. Also, for example, confidence determination engine 125 mayidentify the term “joe@exampleurl.com” and determine that, based on theterm having a format that is indicative of an email address, the termmay be related to an “event attendee” event property.

In some implementations, confidence determination engine 125 may utilizepart of speech taggers, named entity taggers, text parsers, classifiers,and/or other natural language processing components to determineinformation of a message that may be related to an event. For example,confidence determination engine 125 may utilize output of a named entitytagger to identify text of a message that is related to a place. Theconfidence determination engine 125 may determine an event locationbased on the tagged place, optionally utilizing an entity databaseand/or other database to determine if the tagged place is a potentialevent location (e.g., based on one or more properties of the taggedplace in the entity database). Also, for example, the confidencedetermination engine 125 may utilize a parse tree output of a textparser that includes part of speech tags for text of a text segment, anda mapping defining the associations between the text of the textsegment. For example, for a message that includes a segment “Let's plana dinner next Saturday”, parse tree output may be utilized to identifythe term “plan” is a verb, the term “dinner” is the object of the verb,and the term “Saturday” is a proper noun related to a date and thatqualifies the phrase “plan dinner”. In some implementations theconfidence determination engine 125 may utilize an entity database todetermine that the term “plan” is an alias mapped to an entityassociated with an event action, to determine that the term “dinner” isan alias mapped to an entity associated with an event type, and/or todetermine that the term “Saturday” is an alias mapped to an entityassociated with an event date. Based on the parse tree output and/or theentity database mapping, the confidence determination engine 125 maydetermine the segment relates to an event and may identify an entityassociated with the term “dinner” as an event type event property and anentity associated with the term “Saturday” as an event date eventproperty.

As another example, confidence determination engine 125 may identify“Restaurant 1” in a message and additionally identify “next Thursday” inthe message. Confidence determination engine 125 may determine an eventthat includes the event properties “Restaurant 1” as an “event location”event property for the event and “next Thursday” and/or a determineddate for “next Thursday” as an “event date” event property based on theterms they were identified in the message.

Confidence determination engine 125 may determine an event confidencelevel based on the identified event properties in the message or messagetrail. Generally, the confidence level is indicative of a likelihoodthat the determined event is of interest to the user. In someimplementations, a greater number of event properties and/or a greaterprominence (e.g., frequency, position) of event properties in a messagewill result in a determined confidence level that is more indicative ofconfidence than a determined confidence level based on a lesser numberof event properties and/or a lesser prominence of event properties in amessage. For example, confidence determination engine 125 may determinean event confidence level based on a quantity of event properties in themessage. For example, the more event properties that are present in themessage, the more indicative of confidence the confidence level may be.Also, for example, confidence determination engine 125 may furtherdetermine an event confidence level based on a prominence of eventproperties in the message. For example, the more prominently featuredevent properties are in the message, the more indicative of confidencethe confidence level may be. For example, presence of a set of one ormore event properties in the subject of a message or the first paragraphof a message, standing alone, may be more indicative of confidence thanpresence of the set in the last paragraph of a multi-paragraph messagestanding alone.

In some implementations, confidence levels may be determined for each ofone or more event properties of the determined event and an overallconfidence level determined based on the confidence levels. For example,confidence determination engine 125 may determine a confidence levelassociated with an “event location” event property, an “event date”event property, and “event date” event property, an “event type” eventproperty, and/or an “event attendee” event property. The confidencelevel for an individual event property may be based on, for example, theprominence (e.g., frequency, position) of the event property in themessage and/or the clarity of the event property in the message. Forexample, the more prominently featured an event property is in themessage, the more indicative of confidence the confidence level may be.For example, an event property occurring in the subject of a message orthe first paragraph of a message, standing alone, may be more indicativeof confidence than presence of the event property in the last paragraphof a multi-paragraph message standing alone. Also, for example, the moreclear an event property is in the message, the more indicative ofconfidence the confidence level may be. Clarity may be based on, forexample, how many potential conflicting members of the event propertyare indicated by the message and/or the prominence of one or more of thepotentially conflicting members. For example, a message pertaining toplanning a dinner may mention “Restaurant 1” and mention “Restaurant 2”(e.g., “Would you all like to go to Restaurant 1 or Restaurant 2?”).Since two potentially conflicting members of an “event location” eventproperty are present, the confidence level for the event property may beless indicative of confidence than if only one of the members werepresent. However, if “Restaurant 2” is mentioned more prominently then“Restaurant 1” (e.g., if the message is part of a message trail andseveral people have weighed in on the preference for “Restaurant 2”),then the confidence level for the “event location” event property may bemore indicative of confidence.

Confidence determination engine 125 may determine an overall eventconfidence level based on the event property confidence levels. One ormore techniques may be utilized to determine the overall eventconfidence level based on multiple individual confidence levels. Forexample, a weighted and/or unweighted average of one or more of theindividual confidence levels may be utilized. Also, for example, a sumof the confidence levels may additionally and/or alternatively beutilized. Additional and/or alternative techniques may be utilized todetermine a confidence level for an event based on a message.

Referring to FIG. 3, an example message is provided. The message is amessage trail 300 that includes an initial message 305 and a replymessage 310. In some implementations, confidence determination engine125 may identify initial message 305 and determine an event and aconfidence level for the event based on one or more terms in the initialmessage 305. In some implementations, confidence determination engine125 may determine an event and a confidence level for the event based onidentifying one or more terms in message trail 300. In someimplementations, confidence determination engine 125 may identify theinitial message 305, determine an event and a confidence level for theevent based on the terms in initial message 300, subsequently identifyreply message 310 as additional data, and determine a new confidencelevel for the event based on one or more terms in the reply message 310.

In some implementations, confidence determination engine 125 mayidentify one or more terms in initial message 305 that are indicative ofan event. For example, confidence determination engine 125 may identifythe term “dinner” as a term that is associated with an event type. Forexample, confidence determination engine 125 may identify an entity incontent database 115 with an alias of “dinner” and that is associatedwith a “party” entity and/or an “event” entity. Also, for example,confidence determination engine 125 may identify “Thursday” as a timethat may be associated with an “event date” event property of an event.Also, for example, confidence determination engine 125 may identify“Joe,” “Jim,” and/or “User” (based on the terms in the body and/or thee-mail addresses in the To:/From: lines) as people that may beassociated with an “event attendees” event property of an event.

In some implementations, confidence determination engine 125 maydetermine a confidence level for the event based on the terms that wereidentified in initial message 305. Confidence determination engine 125may utilize one or more techniques to determine the confidence level,such as techniques described herein. For example, confidencedetermination engine 125 may determine that initial message 305 includesone or more terms that are associated with an “event attendees” eventproperty, with an “event type” event property, and with an “event date”event property and determine a confidence level based on the messageincluding those terms but not terms that are associated with an “eventlocation” event property and an “event time” event property. Asdescribed herein, an effect on dissemination of information related tothe event may be determined based on the determined event confidencelevel.

In some implementations, confidence determination engine 125 mayidentify additional data that may be related to the determined event. Insome implementations, the additional data may be an additional messagethat is related to the message or message trail that was utilized todetermine the event. In some implementations, the additional data mayadditionally and/or alternatively include data based on one or moreactions of a user. For example, additional data may include data relatedto a search query submitted by the user that includes one or more termsthat are associated with event properties of the determined event. Also,for example, the additional data may include a document navigated toand/or a search result selected by the user that may be associated withone or more event properties of the determined event. Also, for example,additional data may include data related to a locational query of theuser that seeks directions to a location that is associated with an“event location” event property of the event.

In some implementations, the identified additional data may be a messagethat is related to the message or message trail that was utilized todetermine the event and the confidence level. For example, theadditional data may be a new message that is directly associated (e.g.,a “reply”) with the message or message trail that was utilized todetermine the event. Also, for example, the additional data may be amessage that is not directly associated with the message or messagetrail that was utilized to determine the event, but that includesinformation that includes information that indicates it is associatedwith the message or message trail and/or associated with the determinedevent. For example, an email message may be utilized to determine theevent and the confidence level that is associated with the event, and atext message that includes the same users as the email may be identifiedas additional information. Also, for example, an email message may beutilized to determine the event and the confidence level that isassociated with the event, and a separate email message that is not areply to the message may be identified as additional information if itincludes for example, the same or similar recipients, the same orsimilar subject line, one or more terms of the body of the message incommon, and/or the same or similar event properties. For example,confidence determination engine 125 may identify an additional messageas additional data, wherein the message includes a similar subject lineas the first message and/or has one or more terms in common with thefirst message.

In some implementations, the confidence determination engine 125 mayutilize the additional data to determine a new confidence level for theevent. For example, a new message that is identified by confidencedetermination engine 125 as additional data may include one or moreterms that may be utilized to identify additional data associated withone or more event properties. The additional data may be related to oneor more new event properties of the event and/or to the existing eventproperties of the event. A new confidence level may be determined forthe event based on the additional data.

For example, referring to FIG. 3, an example of utilizing an additionalmessage that is associated with the event as additional data isdescribed. Confidence determination engine 125 may initially identifyinitial message 305 and determine an event and an associated confidencelevel based on the terms of the initial message 305. Confidencedetermination engine 125 may identify reply message 310 as additionaldata related to the initial message 305. For example, confidencedetermination engine 125 may be a component of a messaging system andidentify reply message 310 as a reply to initial message 305. Also, forexample, confidence determination engine 125 may additionally and/oralternatively determine that the reply message is additional dataassociated with the initial message based on, for example, the initialmessage 305 and the reply message 310 including the same users assenders and/or recipients, similar terms, and/or similar subjectheadings.

Confidence determination engine 125 may identify one or more terms inreply message 310 that may be associated with an event. For example,confidence determination engine 125 may identify the term “8:30” anddetermine that the term is a time based on the format of the term.Confidence determination engine 125 may determine that “8:30” may beassociated with an “event time” event property. Also, for example, replymessage 310 includes indications of the people that were identified ininitial message 300 and additionally includes a reference to “Bob,”which may be associated with an “attendee” event property. Also, forexample, reply message 310 includes the term “Bob's house” andconfidence determination engine 125 may identify an entity associatedwith “Bob's house” that is additionally associated with a “location”property and/or “location” entity. Confidence determination engine 125may determine that “Bob's house” may be a term that is associated withan “event location” event property.

Confidence determination engine 125 may determine a new confidence levelfor the event associated with initial message 305 based on theadditional data that was identified in reply message 310. Confidencedetermination engine 125 may adjust the previously determined confidencelevel based on the additional data to determine the new confidence leveland/or determine the new confidence level based on the terms of message305 and 310. For example, confidence determination engine 125 maydetermine a new confidence level based on message 310 that is moreindicative of confidence than the confidence level based on message 305alone. For example, the new confidence level may be more indicative ofconfidence since the message 310 includes “event time” and “eventlocation” event properties that were not included in the initial message305.

The immediately preceding example is an example of determining a newconfidence level that is more indicative of confidence based onadditional event properties being present in a new message. In someimplementations, a new confidence level that is more indicative ofconfidence may additionally and/or alternatively be determined based onincreased clarity of one or more event properties. For example, a firstmessage may mention “event location” event properties of “Restaurant 1and Restaurant 2”, and a subsequent message may mention only “Restaurant2”. Based on the increased prominence of “Restaurant 2”, the “eventlocation” event property may be clarified, and a new confidence levelthat is more indicative of confidence determined. Also, in someimplementations, a new confidence level that is less indicative ofconfidence may be determined based on decreased clarity of one or moreevent properties. For example, a first message may mention an “eventlocation” event property of “Restaurant 1” only, and a subsequentmessage may mention a conflicting member of the event property such as“Restaurant 2” (e.g., “I don't like Restaurant 1, how about Restaurant2?”). Based on the addition of the conflicting member “Restaurant 2”,the “event location” event property may be less clear, and a newconfidence level that is less indicative of confidence determined.

Confidence determination engine 125 may identify further additionalmessages that are associated with message trail 300 and may determineadditional event properties and/or event confidence levels whenadditional data is identified in subsequent related messages. Forexample, further new confidence levels for an event may be determined asadditional messages are identified by confidence determination engine125. As described herein, additional messages may include informationthat may result in further new confidence levels that are either moreindicative of confidence in the event, or less indicative of confidencein the event.

In some implementations, confidence determination engine 125 mayidentify data that was submitted by the user and utilize the submitteddata to determine a new confidence level for the determined event. Forexample, a user may submit a search query of “Where is Restaurant 1” andconfidence determination engine 125 may identify the query as additionalinformation. Confidence determination engine 125 may have determined anevent that includes “Restaurant 1” as an event location and determine anew confidence level based on identifying the user submitting a querythat includes “Restaurant 1.” Confidence determination engine 125 maydetermine a confidence level that is more indicative of the event havinginterest to the user based on identifying the query associated with theterm “Restaurant 1.” Also, for example, a user may submit a search querythat includes the term “Where are good places for birthday parties” andidentify an event that is associated with an “event type” event propertyof “birthday party,” and determine a new confidence level for the eventthat is more indicative of user interest in the event based onidentifying a search query submitted by the user that includes “birthdayparty”.

Application system 130 includes one or more applications that maydisseminate information to a user and/or one or more applications thatinterface with applications that may disseminate information to a user.For example, application system 130 may include a calendar applicationand/or a component of a calendar application that may disseminateinformation associated with events, such as notifications of upcomingevents and/or recommendations for events to add to a user's calendar.Also, for example, application system 130 may include a recommendationapplication that disseminates information associated withrecommendations to events, locations, etc. Also, for example,application system 130 may include a search engine that receives asearch query, identifies documents responsive to the search query, andgenerates search results based on the identified documents. Also, forexample, application system 130 may include a query suggestion systemthat receives a query (such as a partial query) and identifies one ormore query suggestions based on the query (such as an autocompletesuggestion in the case of a partial query).

Also, for example, the application system 130 may include one or moreapplications that interface with applications that may disseminateinformation to a user. For example, the application system 130 maydetermine and/or alter data stored in one or more databases utilized byapplications that may disseminate information to a user. Such data maybe utilized by the respective application(s) to effect dissemination ofinformation related to the event. For example, the application system130 may provide data that is indicative of one or more of the eventproperties of an event and that indicates whether, and/or to whatextent, one or more applications should utilize the event properties toinfluence information disseminated by the application.

For example, the application system 130 may determine, based on aconfidence level for an event, that event properties for the eventshould be utilized by a search engine to influence ranking of searchresult documents, but should not be utilized at all by a calendarapplication. Based on such a determination, the application system 130may provide information related to the event properties to a databaseutilized by the search engine, but not provide the information (orprovide it with a “don't use” flag) to a database utilized by thecalendar application. As another example, the application system 130 maydetermine, based on a confidence level for an event, that eventproperties for the event should be utilized by a search engine toinfluence ranking of search result documents, and utilized by arecommendation engine to influence ranking of recommendations providedto a user. Based on such a determination, the application system 130 mayprovide information related to the event properties to a databaseutilized by the search engine, and provide information related to theevent properties to a database utilized by the recommendation engine. Inimplementations in which the search engine and recommendation engine mayutilize the same database, the information may be provided to thedatabase, with flags and/or other indication that indicate the searchengine and the recommendation engine should both utilize the informationto influence respective of search results and recommendations.

In some implementations, confidence determination engine 125 may,directly or indirectly (e.g., via content database 115), provideapplication system 130 with event properties of a determined event,and/or one or more confidence levels associated with the event. Forexample, confidence determination engine 125 may determine an event froma message, determine a confidence level for the event based on the termsof the message, and provide the determined confidence level with one ormore event properties to application system 130. Confidencedetermination engine 125 may further provide application system 130 witha new confidence level for the event and additional event properties (ifany) when additional data is identified and the new confidence level isdetermined based on the additional data.

Application system 130 may determine an effect on dissemination, to auser, of information related to an event of the user. As describedherein, the effect on dissemination of information may be based on adynamic confidence level for the event, and the effect may changeresponsive to changes in the confidence level. For example, theapplication system 130 may determine whether and/or to what extent todisseminate information related to an event to a user based on theconfidence level associated with the event. For example, inimplementations where the application system 130 includes an applicationthat provides a notification related to the event (e.g., a reminderand/or a recommendation related to the event), the application may onlyprovide a notification when the confidence level associated with theevent satisfies a threshold value. Also, for example, in implementationswhere the application system 130 includes an application that ranks oneor more items of content based on the event (e.g., promoting searchresults, recommendations, and/or query suggestions that relate to theevent), the application may only rank the content based on the eventwhen the confidence level associated with the event satisfies athreshold value. Also, for example, in implementations where theapplication system 130 includes an application that provides anotification related to the event (e.g., a reminder and/or a suggestionrelated to the event), the format of the notification, the content ofthe notification, and/or the time at which the notification is providedmay be determined based on the confidence level associated with theevent. Also, for example, in implementations where the applicationsystem 130 includes an application that ranks one or more items ofcontent based on the event, the extent of ranking based on the event(e.g., the weighting of a ranking signal based on the event) may bedetermined based on the confidence level associated with the event.

In some implementations, the extent to which event properties of anevent are utilized to determine and/or rank disseminated informationrelated to an event, may be based on individual confidence levels thatmay optionally be determined for the event properties as describedherein. For example, a first event property of an event that has aconfidence level indicative of high confidence in the event property maymore strongly influence determination and/or ranking of information thana second event property of an event that has a confidence levelindicative of low confidence in the event property.

In some implementations, determining an effect on dissemination ofinformation related to the event includes determining an effect for afirst dissemination of information related to the event and determiningan effect for a second dissemination of information related to theevent, wherein the first and second dissemination of information areunique. For example, in some implementations, determining an effect ondissemination of information related to the event includes individuallydetermining an effect for each of a plurality of unique applicationsthat may disseminate information. The criteria for determining when,and/or to what extent, to effect dissemination of information for agiven application may be unique from the criteria of one or more otherapplications. For example, for a first application, information may beprovided that is determined and/or influenced based on the event whenthe confidence level satisfies a first threshold. However, for a secondapplication, information may be provided that is determined and/orinfluenced based on the event only when the confidence level satisfies asecond threshold that is unique from the first threshold. For example, arecommendation application may provide a recommendation that isdetermined and/or influenced based on the event only when the confidencelevel is greater than 25%, whereas a calendar application may provide anotification that is determined and/or influenced based on the eventonly when the confidence level is greater than 50%.

In some implementations, the disseminated information may include anotification to the user and determining the effect may includedetermining whether to provide the notification based on the confidencelevel associated with the event. A notification may include, forexample, a prompt to the user that an event has been created in acalendar of the user based on one or more messages. Also, for example, anotification may be a reminder to the user of an upcoming event. Also,for example, a notification may be a recommendation to the user that isassociated with an upcoming event such as a recommendation related to anevent location associated with the event (the same event location or arelated event location). In some implementations, the notification maybe provided to the user when the confidence level associated with theevent satisfies a threshold. For example, application system 130 may beprovided with an event with a confidence level of 30% and applicationsystem 130 may not send a notification to the user based on theconfidence level not satisfying a threshold. Confidence determinationengine 125 may determine a new confidence level of 55% for the eventbased on additional data and application system 130 may provide anotification if 55% satisfies a threshold value to providenotifications.

In some implementations, the disseminated information may include anotification to the user and determining the effect may includedetermining the format of the notification, the content of thenotification, and/or the time at which the notification is provided. Forexample, application system 130 may be provided with an event with aconfidence level of 30% and application system 130 may only provide anon-obtrusive notification to the user based on the confidence level.For example, the non-obtrusive notification may include highlighting orother emphasis of information related to the event (e.g., highlighting amessage associated with the event and/or highlighting event propertiesin the message). The emphasis may notify the user of a potential eventand optionally enable the user to create a calendar entry and/or otherentry by interfacing with the emphasized aspects. If the applicationsystem 130 is provided with an event with a confidence level of greaterthan 70% (e.g., based on a new confidence level), the application system130 may provide a more obtrusive notification to the user based on theconfidence level. For example, the more obtrusive notification mayinclude a pop-up or other notification that includes information relatedto the event. The pop-up or other notification may notify the user of apotential event and optionally enable the user to create a calendarentry and/or other entry related to the event.

Referring to FIG. 4, an example notification is provided. Thenotification may be provided to the user, for example, if the confidencelevel associated with the determined event satisfies a threshold. Thenotification includes event properties of the event, such as an eventname, an event location, and a list of attendees. The event propertiesmay be determined by confidence determination engine 125 based on termsin one or more messages and/or additional data as described herein. Theevent may be populated in a calendar or other event database of the userif the user selects “OK.” The user may select “CANCEL” if the user doesnot have interest in the event being populated in the calendar at thattime. In some implementations, the user may be prompted again if theconfidence level associated with the event increases based on additionaldata that is identified by confidence determination engine 125. In someimplementations, the event may be deleted and further effects ondissemination of information related to the event suppressed if the userselects “CANCEL.”

Referring to FIG. 5, another example notification is provided. In someimplementations, the notification may be provided by application system130 in response to identifying an upcoming event of the user. Forexample, the notification may be provided to the user one hour beforethe start of an event. In some implementations, application system 130may utilize the confidence level of the determined event to affectwhether to provide the notification to the user. For example, thenotification may be provided to the user if the confidence level of thedetermined event satisfies a threshold. Also, for example, the remindernotification may be provided to the user only at a certain time, such asone hour before the event, if the associated confidence level satisfiesa threshold at that time. In some implementations, the notification ofFIG. 4 may be provided if the confidence level satisfies a firstthreshold and the notification of FIG. 5 may additionally and/oralternatively be provided if the confidence level satisfies a secondthreshold unique from the first threshold. For example, the notificationof FIG. 4 may be provided based on an initial confidence leveldetermined for the event, but the notification of FIG. 5 may not beprovided based on the initial confidence level. A new confidence levelmay then be determined for the event (based on additional data asdescribed herein), and the notification of FIG. 5 may be provided basedon the new confidence level.

In some implementations, application system 130 may include a searchengine and determining the effect may include determining, based on aconfidence level for an event, whether and/or to what extent to ranksearch result documents based on the event. For example, the searchengine may promote certain search result document based on the event,such as search result documents that are associated with one or moreevent properties of the event. For example, a search result document maybe associated, in an index and/or other database, with informationidentifying one or more terms and/or entities related to the searchresult document and the ranking of the search result document for agiven query may be increased if one or more of those terms and/orentities is related to an event property.

In some implementations, if the confidence level for an event is above athreshold, the search engine may rank search results based on the event.Additionally or alternatively, in some implementations, the searchengine may rank search results based on the event, wherein the influenceof the event on the ranking is based on the confidence level. Forexample, an event with a confidence level of 80% may influence theranking more than an event with a confidence level of 40%. In someimplementations, any optional confidence levels determined forindividual event properties may be utilized to determine what extent torank search results based on the event properties. For example, in someimplementations, the search engine may only rank search results based ona given event property if a confidence level associated with that eventproperty satisfies a threshold. Additionally or alternatively, in someimplementations, the search engine may rank search results based on agiven event property, wherein the influence of the given event propertyon the ranking is based on the confidence level of the given eventproperty. For example, a first event property with a confidence level of80% may influence the ranking more than a second event property with aconfidence level of 40%.

In some implementations, application system 130 may include a querysuggestion engine and determining the effect may include determining,based on a confidence level for an event, whether and/or to what extentto rank query suggestions based on the event. For example, the querysuggestion engine may promote certain search query suggestions based onthe event, such as query suggestions that are associated with one ormore event properties of the event. For example, a potential querysuggestion for a given query may include or otherwise be associated withone or more terms and/or entities and the ranking of the potential querysuggestion may be increased if one or more of those terms and/orentities is related to an event property. Query suggestions may includequery suggestions for a submitted query such as a recommendation for analternative query that is related to a submitted query. Querysuggestions may additionally or alternatively include query suggestionsfor a partial query such as a recommendation for a query that isdetermined based on one or more characters of the partial query. Forexample, a query suggestion for the partial query “Re” may be“Restaurant 1”.

In some implementations, if the confidence level for an event is above athreshold, the query suggestion engine may rank query suggestions basedon the event. Additionally or alternatively, in some implementations,the query suggestion engine may rank query suggestions based on theevent, wherein the influence of the event on the ranking is based on theconfidence level. For example, an event with a confidence level of 80%may influence the ranking more than an event with a confidence level of40%. In some implementations, any optional confidence levels determinedfor individual event properties may be utilized to determine what extentto rank query suggestions based on the event properties. For example, insome implementations, the query suggestion engine may only rank querysuggestions based on a given event property if a confidence levelassociated with that event property satisfies a threshold. Additionallyor alternatively, in some implementations, the query suggestion enginemay rank query suggestions based on a given event property, wherein theinfluence of the given event property on the ranking is based on theconfidence level of the given event property. For example, a first eventproperty with a confidence level of 80% may influence the ranking morethan a second event property with a confidence level of 40%.

As described, in some implementations, the query suggestion engine mayprovide the query suggestions to a user via computing device 105 inresponse to one or terms that were provided by the user via clientdatabase 105. In some implementations, ranking of the query suggestionsmay be utilized to determine which query suggestions are provided to auser and/or in which order the query suggestions are displayed to theuser. In some implementations, ranking one or more query suggestionsthat are associated with a determined event may include boosting thequery suggestions in a ranked list to increase the likelihood that theuser will be provided the query suggestions. For example, a scoringassociated with a query suggestion related to the event may be increasedto a score that is higher than the score the query suggestion wouldotherwise have in a list of ranked query suggestions.

Referring to FIG. 6A, an example of providing query suggestions to auser is illustrated. In FIG. 6A, the ranking of the query suggestions isnot being influenced based on an event based on, for example, adetermination to not influence the ranking based on a low confidencelevel of the event. In FIG. 6A, a user has entered the partial searchquery “re” into a search field representation 600A and a drop down menu605A of query suggestions is displayed. The query suggestion engine mayidentify one or more candidate query suggestions that may be associatedwith the partial query “re”. For example, the query suggestion enginemay use prefix based matching and/or other techniques to identifycandidate query suggestions. Application engine 130 may identify querysuggestions based on, for example, a list of past user queries, a listof automatically generated queries, and/or real time automaticallygenerated queries. The drop down menu 605A includes four querysuggestions that are based on the partial search query “re”.

Referring to FIG. 6B, an example of providing query suggestion resultsto a user is illustrated, wherein the ranking of the query suggestionsis influenced by an event. For example, the event may include an “eventlocation” event property of “Restaurant 1” and a determination may bemade to influence the ranking based on a high confidence level of theevent. The ranking of the query suggestion “restaurant 1” is promoted inFIG. 6B based on the similarity of the query suggestion to the “eventlocation” event property of “Restaurant 1”. Similarity may bedetermined, for example, based on textual similarity and/or similarityof one or more entities associated with the query suggestion and the“event location” event property.

Referring to FIG. 2, a flow chart illustrating an example method ofdetermining an effect on dissemination of information related to anevent is provided. Other implementations may perform the steps in adifferent order, omit certain steps, and/or perform different and/oradditional steps than those illustrated in FIG. 2. For convenience,aspects of FIG. 2 will be described with reference to one or morecomponents of FIG. 1 that may perform the method, such as confidencedetermination engine 125 and/or application system 130.

At step 200, a message that is associated with a user is identified. Insome implementations, the identified message may be a message trail ofone or more related messages. Messages and/or message trails may includeone or more terms that may be identified in, for example, the body ofthe message, subject lines of the message, and/or contact information ofsenders and/or recipients of the message. Messages may include, forexample, emails, text messages, instant messages, and/or social mediapostings.

At step 205, an event is determined based on the message. An eventincludes one or more event properties that are indicative of the event,such as an event date, an event location, an event type, and/or one ormore attendees of the event. In some implementations, one or more eventproperties may be determined based on one or more terms that areidentified in the message that was identified at step 200. For example,confidence determination engine 125 may identify “Restaurant 1” in themessage, determine that “Restaurant 1” is a location based onidentifying an entity with an alias of “Restaurant 1” that is associatedwith a “location” entity in content database 115, and determine anentity that includes “Restaurant 1” as an “event location” eventproperty.

At step 210, an event confidence level is determined for the event. Theevent confidence level is indicative of likelihood that the user hasinterest in being associated with the event. The confidence level may bedetermined based on one or more event properties that have beenidentified in the message or message trail. For example, confidencedetermination engine 125 may identify an “event location” and one ormore “event attendees” in a message and determine a confidence levelbased on the determined event properties. The event confidence level maybe provided to application system 130 and/or another component byconfidence determination engine 125.

At step 215, an effect on dissemination of information related to theevent is determined. Dissemination of information may include, forexample, providing one or more notifications to a user, providing searchresults to a user, and/or providing search query suggestions to a user.Determining an effect on information dissemination may includedetermining whether to provide a notification related to the event to auser based on the confidence level, what type of notification toprovide, determining whether and/or to what extent to rank one or moresearch results that are related to the event, and/or whether and/or towhat extent to rank one or more query suggestions that are related to anevent.

As described herein, in some implementations, determining an effect ondissemination of information related to the event includes determiningan effect for a first dissemination of information related to the eventand determining an effect for a second dissemination of informationrelated to the event, wherein the first and second dissemination ofinformation are unique. For example, the first dissemination ofinformation may relate to a first application that may provideinformation to the user and the second dissemination of information mayrelate to a second application, that is unique from the firstapplication and that may provide information to the user. For example,the first application may be an e-mail application, such as an emailapplication accessible via a web browser or other application executingon a computing device of the user; and the second application may be asearch engine to which the user may submit queries via a computingdevice and receive information from the search engine in response to thequeries.

At step 220, additional data associated with the event and/or the useris identified. Additional data may include one or more messages that aredetermined to be associated with the message and/or message trail thatwas utilized to determine the event. Additionally or alternatively,additional data may include data based on one or more actions of theuser. For example, additional data may include one or more submittedsearch terms of the user, one or more search results that were selectedby the user, and/or a navigational query of the user.

At step 225, a new confidence level is determined for the event based onthe additional data. In some implementations, one or more terms may beidentified from the additional data and the terms may be utilized todetermine the new confidence level. For example, the additional data maybe a new message that is associated with a previous message that wasutilized to determine the event and the confidence level of step 210.The new message may include, for example, the term “8:30” that was notincluded in the message of step 200. Confidence determination engine 125may determine that “8:30” is likely a time and associate “8:30” with theevent as an “event time” event property. A new confidence level may bedetermined for the event based on the additional “event time” eventproperty. New confidence levels may be based on additional and/oralternative factors, such as those described herein. For example, thenew confidence level may be based on identifying repeated eventproperties in additional data, conflicting event properties inadditional data, and/or identifying more specific event properties inadditional data. Confidence determination engine 125 may determine thenew confidence level associated with the event and provide applicationsystem 130 and/or another component with the new confidence level.

At step 230, the effect on dissemination of information related to theevent is adjusted. In some implementations, application system 130 mayutilize the new confidence level to determine when and/or howinformation related to the event is disseminated to the user. Forexample, application system 130 may provide a notification to the userthat is related to the event based on the new confidence levelsatisfying a threshold value, wherein the threshold value was notsatisfied by the previous confidence level associated with the event.Also, for example, application system 130 may rank one or more searchresults based on the new confidence level, wherein search results werenot ranked or ranked to a lesser extent based on the previous confidencelevel associated with the event.

Also, for example, based on the previous confidence level associatedwith the event, it may have been determined to provide a firstdissemination of information related to a first application, but notprovide a second dissemination of information related to a secondapplication that is unique from the first application. However, based onthe new confidence level, it may be determined to provide both the firstdissemination of information and the second dissemination ofinformation. As described herein, additional confidence levels may bedetermined for an event based on further additional information and theeffect on dissemination of information further adjusted. For example,steps 220, 225, and 230 may be repeated one or more times.

In situations in which the systems described herein collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, email, social actions or activities, browsing history, auser's preferences, or a user's current geographic location), or tocontrol whether and/or how to receive content from the content serverthat may be more relevant to the user. Also, certain data may be treatedin one or more ways before it is stored or used, so that personallyidentifiable information may be removed. For example, a user's identitymay be treated so that personally identifiable information may not bedetermined for the user, or a user's geographic location may begeneralized where geographic location information may be obtained (suchas to a city, ZIP code, or state level), so that a particular geographiclocation of a user may not be determined. Thus, the user may havecontrol over how information is collected about the user and/or used.

FIG. 7 is a block diagram of an example computer system 710. Computersystem 710 typically includes at least one processor 714 whichcommunicates with a number of peripheral devices via bus subsystem 712.These peripheral devices may include a storage subsystem 724, including,for example, a memory subsystem 726 and a file storage subsystem 728,user interface input devices 722, user interface output devices 720, anda network interface subsystem 716. The input and output devices allowuser interaction with computer system 710. Network interface subsystem716 provides an interface to outside networks and is coupled tocorresponding interface devices in other computer systems.

User interface input devices 722 may include a keyboard, pointingdevices such as a mouse, trackball, touchpad, or graphics tablet, ascanner, a touchscreen incorporated into the display, audio inputdevices such as voice recognition systems, microphones, and/or othertypes of input devices. In general, use of the term “input device” isintended to include all possible types of devices and ways to inputinformation into computer system 710 or onto a communication network.

User interface output devices 720 may include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices. The display subsystem may include a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), a projectiondevice, or some other mechanism for creating a visible image. Thedisplay subsystem may also provide non-visual display such as via audiooutput devices. In general, use of the term “output device” is intendedto include all possible types of devices and ways to output informationfrom computer system 710 to the user or to another machine or computersystem.

Storage subsystem 724 stores programming and data constructs thatprovide the functionality of some or all of the modules describedherein. For example, the storage subsystem 724 may include the logic todetermine an event from one or more messages of a user, determine aconfidence level that is indicative of user interest in the event,identify additional data associated with the message and/or event,determine a new confidence level based on the additional data, and/oradjust the effect of the confidence level on the dissemination ofinformation related to the event. These software modules are generallyexecuted by processor 714 alone or in combination with other processors.Memory 726 used in the storage subsystem can include a number ofmemories including a main random access memory (RAM) 730 for storage ofinstructions and data during program execution and a read only memory(ROM) 732 in which fixed instructions are stored. A file storagesubsystem 728 can provide persistent storage for program and data files,and may include a hard disk drive, a floppy disk drive along withassociated removable media, a CD-ROM drive, an optical drive, orremovable media cartridges. The modules implementing the functionalityof certain implementations may be stored by file storage subsystem 728in the storage subsystem 724, or in other machines accessible by theprocessor(s) 714.

Bus subsystem 712 provides a mechanism for letting the variouscomponents and subsystems of computer system 710 communicate with eachother as intended. Although bus subsystem 712 is shown schematically asa single bus, alternative implementations of the bus subsystem may usemultiple busses.

Computer system 710 can be of varying types including a workstation,server, computing cluster, blade server, server farm, or any other dataprocessing system or computing device. Due to the ever-changing natureof computers and networks, the description of computer system 710depicted in FIG. 7 is intended only as a specific example for purposesof illustrating some implementations. Many other configurations ofcomputer system 710 are possible having more or fewer components thanthe computer system depicted in FIG. 7.

While several implementations have been described and illustratedherein, a variety of other means and/or structures for performing thefunction and/or obtaining the results and/or one or more of theadvantages described herein may be utilized, and each of such variationsand/or modifications is deemed to be within the scope of theimplementations described herein. More generally, all parameters,dimensions, materials, and configurations described herein are meant tobe exemplary and that the actual parameters, dimensions, materials,and/or configurations will depend upon the specific application orapplications for which the teachings is/are used. Those skilled in theart will recognize, or be able to ascertain using no more than routineexperimentation, many equivalents to the specific implementationsdescribed herein. It is, therefore, to be understood that the foregoingimplementations are presented by way of example only and that, withinthe scope of the appended claims and equivalents thereto,implementations may be practiced otherwise than as specificallydescribed and claimed. Implementations of the present disclosure aredirected to each individual feature, system, article, material, kit,and/or method described herein. In addition, any combination of two ormore such features, systems, articles, materials, kits, and/or methods,if such features, systems, articles, materials, kits, and/or methods arenot mutually inconsistent, is included within the scope of the presentdisclosure.

What is claimed is:
 1. A computer implemented method, comprising:identifying a message of a user, wherein the message includes aplurality of terms and is an electronic communication sent or receivedby the user; determining an event based on the message, wherein theevent includes one or more event properties that are determined based onone or more of the terms; determining an event confidence level based onthe event properties; determining an effect on dissemination ofinformation related to the event, wherein the effect is determined basedon the event confidence level, wherein the dissemination of informationincludes a first dissemination of information that is related to a firstcomputer application and a second dissemination of information that isrelated to a second computer application and that is unique from thefirst dissemination of information, and wherein the determining theeffect on the dissemination of information comprises: determining, basedon the event confidence level, to influence the first dissemination ofinformation based on the event and to not influence the seconddissemination of information based on the event, the influence of thefirst dissemination of information being reflected in output provided tothe user via the first computer application; identifying additional dataassociated with the user and the event; determining a new eventconfidence level based on the additional data; and adjusting the effecton the dissemination of information related to the event based on thenew event confidence level, wherein the adjusting the effect on thedissemination of information comprises determining, based on the newevent confidence level, to influence both the first dissemination ofinformation and the second dissemination of information based on theevent, the influence of the second dissemination of information beingreflected in additional output provided to the user via the secondcomputer application.
 2. The method of claim 1, wherein the eventproperties are related to one or more of attendees of the event, eventlocation, event type, and event time.
 3. The method of claim 1, whereinidentifying the additional data includes identifying a new message ofthe user that is associated with the message and that is receivedsubsequent to the message.
 4. The method of claim 1, wherein theadditional data is based on one or more actions of the user.
 5. Themethod of claim 4, wherein the additional data is based on a submittedsearch query of a user search query action of the one or more actions.6. The method of claim 1, wherein the second dissemination ofinformation includes providing one or more search results to the user;wherein the determining to not influence the second dissemination ofinformation includes determining, based on the event confidence level,not to rank one or more of the search results based on the event; andwherein determining to influence the second dissemination of informationin the adjusting the effect on the dissemination of information relatedto the event includes determining, based on the new event confidencelevel, to rank one or more of the search results based on the event. 7.The method of claim 1, wherein the first dissemination of informationincludes providing one or more search results to the user; wherein thedetermining to influence the first dissemination of information includesdetermining, based on the confidence level, a first extent to which oneor more of the search results is ranked based on the event; and whereindetermining to influence the first dissemination of information in theadjusting the effect on the dissemination of information related to theevent includes determining, based on the new event confidence level, asecond extent to which one or more of the search results is ranked basedon the event.
 8. The method of claim 1, wherein the first disseminationof information includes providing one or more query suggestions to theuser and wherein the determining to influence the first dissemination ofinformation in the adjusting the effect on the dissemination ofinformation related to the event includes adjusting, based on the newevent confidence level, a degree of influence of the event in rankingthe query suggestions.
 9. The method of claim 1, wherein the new eventconfidence level is determined based on the event confidence level. 10.The method of claim 1, wherein the second dissemination of informationincludes providing a notification to the user and the effect on thesecond dissemination of information is whether to provide thenotification to the user.
 11. The method of claim 1, wherein the firstdissemination of information includes providing a notification to theuser and the effect on the first dissemination of information includesone or more characteristics of the notification to the user.
 12. Asystem including memory and one or more processors operable to executeinstructions in the memory, comprising instructions to: identify amessage of a user, wherein the message includes a plurality of terms andis an electronic communication sent or received by the user; determinean event based on the message, wherein the event includes one or moreevent properties that are determined based on one or more of the terms;determine an event confidence level based on the event properties;determine an effect on dissemination of information related to theevent, wherein the effect is determined based on the event confidencelevel, wherein the dissemination of information includes a firstdissemination of information that is related to a first computerapplication and a second dissemination of information that is related toa second computer application and that is unique from the firstdissemination of information, and wherein the instructions to determinethe effect on the dissemination of information comprise instructions to:determine, based on the event confidence level, to influence the firstdissemination of information based on the event and to not influence thesecond dissemination of information based on the event, the influence ofthe first dissemination of information being reflected in outputprovided to the user via the first computer application; identifyadditional data associated with the user and the event; determine a newevent confidence level based on the additional data; and adjust theeffect on the dissemination of information related to the event based onthe new event confidence level, wherein the instructions to adjust theeffect on the dissemination of information comprise instructions todetermine, based on the new event confidence level, to influence boththe first dissemination of information and the second dissemination ofinformation based on the event, the influence of the seconddissemination of information being reflected in additional outputprovided to the user via the second computer application.
 13. The systemof claim 12, wherein the instructions to identify the additional dataincludes instructions to identify a new message of the user that isassociated with the message and that is received subsequent to themessage.
 14. The system of claim 12, wherein the additional data isbased on one or more actions of the user.
 15. The system of claim 12,wherein the second dissemination of information includes providing oneor more search results to the user; wherein the instructions todetermine to not influence the second dissemination of informationincludes instructions to determine, based on the event confidence level,not to rank one or more of the search results based on the event; andwherein the instructions to determine to influence the seconddissemination of information in the instructions to adjust the effect onthe dissemination of information related to the event includesinstructions to determine, based on the new event confidence level, torank one or more of the search results based on the event.
 16. Thesystem of claim 12, wherein the first dissemination of informationincludes providing one or more search results to the user; wherein theinstructions to determine to influence the first dissemination ofinformation includes instructions to determine, based on the confidencelevel, a first extent to which one or more of the search results isranked based on the event; and wherein the instructions to determine toinfluence the second dissemination of information in the instructions toadjust the effect on the dissemination of information related to theevent includes instructions to determine, based on the new eventconfidence level, a second extent to which one or more of the searchresults is ranked based on the event.
 17. The system of claim 12,wherein the first dissemination of information includes providing one ormore query suggestions to the user and the instructions to determine toinfluence the second dissemination of information in the instructions toadjust the effect on the dissemination of information related to theevent includes instructions to adjust, based on the new event confidencelevel, a degree of influence of the event in ranking the querysuggestions.
 18. The system of claim 12, wherein the new eventconfidence level is determined based on the event confidence level. 19.The system of claim 12, wherein the second dissemination of informationincludes providing a notification to the user and the effect on thesecond dissemination of information is whether to provide thenotification to the user.
 20. The system of claim 12, wherein the firstdissemination of information includes providing a notification to theuser and the effect on the first dissemination of information includesone or more characteristics of the notification to the user.
 21. Anon-transitory computer readable storage medium storing at least oneprogram configured for execution by at least one processor of a computersystem, the at least one program comprising instructions to: identify amessage of a user, wherein the message includes a plurality of terms andis an electronic communication sent or received by the user; determinean event based on the message, wherein the event includes one or moreevent properties that are determined based on one or more of the terms;determine an event confidence level based on the event properties;determine an effect on dissemination of information related to theevent, wherein the effect is determined based on the event confidencelevel, wherein the dissemination of information includes a firstdissemination of information that is related to a first computerapplication and a second dissemination of information that is related toa second computer application and that is unique from the firstdissemination of information, and wherein the instructions to determinethe effect on the dissemination of information comprise instructions to:determine, based on the event confidence level, to influence the firstdissemination of information based on the event and to not influence thesecond dissemination of information based on the event, the influence ofthe first dissemination of information being reflected in outputprovided to the user via the first computer application; identifyadditional data associated with the user and the event; determine a newevent confidence level based on the additional data; and adjust theeffect on the dissemination of information related to the event based onthe new event confidence level, wherein the instructions to adjust theeffect on the dissemination of information comprise instructions todetermine, based on the new event confidence level, to influence boththe first dissemination of information and the second dissemination ofinformation based on the event, the influence of the seconddissemination of information being reflected in additional outputprovided to the user via the second computer application.